Evaluation of Differences in Patient and Physician Perception of Benefit and Risks of Aspirin and Antifibrinolytic Therapy in Cardiac Surgery
Bibliographic record
Abstract
It is unclear whether physicians and patients have similar concerns and preferences when considering benefit and risks of aspirin and antifibrinolytic therapy for cardiac surgery. We surveyed both groups to ascertain their perceptions and preferences for treatment in this setting. Both preoperative and postoperative cardiac surgical patients and the physician craft groups caring for them (cardiology, surgery, anaesthesia/critical care), were provided with estimates of benefits and risks of aspirin and antifibrinolytic therapy. All study participants were asked to stipulate the minimal absolute risk reduction required for them to agree to such therapy. When compared with the cardiac surgical patients they treat, physicians required a smaller thrombotic risk reduction with aspirin whilst accepting its known increased risk of bleeding. This was significantly different in a high-risk stroke setting (incidence 5%) where the required relative risk reduction with aspirin use for physicians was 20% versus patients 40% (P <0.001); and for myocardial infarction, physicians 20% versus patients 36% (P=0.051). For antifibrinolytic therapy, the tolerated increased relative risk of stroke for physicians was 20% versus patients 10% (P=0.004), and for myocardial infarction, physicians 16.7% versus patients 4.2% (P <0.001). The three physician craft groups had comparable tolerances of thrombotic risk. Patient and physician preferences for perioperative aspirin and antifibrinolytic therapy sometimes differ based on risk benefit analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".